Penalized-likelihood reconstruction with high-fidelity measurement models for high-resolution cone-beam imaging

Steven Tilley, Matthew Jacobson, Qian Cao, Michael Brehler, Alejandro Sisniega, Wojciech Zbijewski, J. Webster Stayman

Research output: Contribution to journalArticlepeer-review


—We present a novel reconstruction algorithm based on a general cone-beam CT forward model which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized-Likelihood objective function which incorporates models of Blur and Correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared to deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPL-BC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared to deblurring followed by FDK, a model based method without blur, and a model based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model based methods without blur and/or correlation to a registered µCT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including Trabecular Thickness (Tb.Th.) were computed for each reconstruction approach as well as the µCT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255 mm, as compared to the µCT derived value of 0.193 mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271 mm, 0.309 mm, and 0.335 mm, respectively).

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Dec 14 2017


  • Deconvolution
  • Extremities Imaging
  • Model-based Iterative Reconstruction
  • Noise Correlation
  • Trabecular Bone

ASJC Scopus subject areas

  • General

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